CN113222310B - Goods picking productivity scheduling method and device - Google Patents
Goods picking productivity scheduling method and device Download PDFInfo
- Publication number
- CN113222310B CN113222310B CN202010079850.0A CN202010079850A CN113222310B CN 113222310 B CN113222310 B CN 113222310B CN 202010079850 A CN202010079850 A CN 202010079850A CN 113222310 B CN113222310 B CN 113222310B
- Authority
- CN
- China
- Prior art keywords
- storage area
- event
- scheduling
- personnel
- productivity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000003860 storage Methods 0.000 claims abstract description 178
- 238000004519 manufacturing process Methods 0.000 claims abstract description 73
- 238000004088 simulation Methods 0.000 claims abstract description 66
- 238000007689 inspection Methods 0.000 claims abstract description 35
- 238000009826 distribution Methods 0.000 claims abstract description 26
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 8
- 238000010586 diagram Methods 0.000 description 13
- 238000004891 communication Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000001550 time effect Effects 0.000 description 4
- 238000012384 transportation and delivery Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 239000003795 chemical substances by application Substances 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for dispatching picking productivity, and relates to the technical field of storage. One embodiment of the method comprises the following steps: obtaining a plurality of groups of personnel allocation combinations of each storage area according to personnel allocation conditions of each storage area; each time a dispatch checking time node is reached, distributing combinations for each group of personnel, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency; and dispatching the picking productivity of each storage area according to the total production number of each storage area and the order number completed according to time efficiency corresponding to each group of personnel distribution combination under each dispatching inspection time node. The technical problem of inaccurate productivity scheduling can be solved by the implementation mode.
Description
Technical Field
The invention relates to the technical field of storage, in particular to a method and a device for dispatching picking productivity.
Background
During the production of a warehouse order, the picking operation may typically account for 40% of the total job duration of the warehouse. How to schedule the picking productivity is the key of cost reduction and efficiency improvement of logistics enterprises. In modern warehouse planning, along with the continuous development of information technologies such as 5G, the informatization degree in the warehouse is higher and higher, digital twin related work can be performed according to production data in the warehouse, and the real-time or subsequent production conditions in the warehouse are represented in a simulation mode, so that related capacity scheduling is performed, and the picking efficiency and order aging are improved.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
for the prior art, the production scheduling in the warehouse basically depends on manpower, and the corresponding scheduling is performed according to the production data conditions reflected by the warehouse system, so that the productivity scheduling is inaccurate, and the cost reduction and synergy cannot be maximally realized.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide a method and an apparatus for scheduling picking productivity, so as to solve the technical problem of inaccurate productivity scheduling.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a pick productivity scheduling method, including:
obtaining a plurality of groups of personnel allocation combinations of each storage area according to personnel allocation conditions of each storage area;
each time a dispatch checking time node is reached, distributing combinations for each group of personnel, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency;
and dispatching the picking productivity of each storage area according to the total production number of each storage area and the order number completed according to time efficiency corresponding to each group of personnel distribution combination under each dispatching inspection time node.
Optionally, the scheduling of the picking productivity of each storage area according to the total production number of each storage area and the order number completed in time effect corresponding to each group of personnel distribution combination under each scheduling check time node includes:
according to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node;
and dispatching the picking productivity of each storage area according to the personnel allocation condition of each storage area under each dispatching inspection time node.
Optionally, for each scheduled inspection time node, determining personnel allocation of the respective storage areas under the scheduled inspection time node by adopting the following method:
calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency;
and screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node.
Optionally, for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by the following method:
And according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
Optionally, according to the personnel allocation situation of each storage area, obtaining a plurality of groups of personnel allocation combinations of each storage area, including:
and determining personnel allocation conditions of each storage area in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of each storage area.
Optionally, performing continuous event simulation on the job task of the warehouse-out of each storage area, including:
distributing a pick-up collection list event to each picker, and adding the pick-up collection list event into an event queue;
if the completion time of the pick-up collection list event is reached, the pick-up collection list event is moved out of the event queue, and the pick-up task event corresponding to the pick-up collection list event is added into the event queue;
if the completion time of the picking task event is reached, the picking task event is moved out of the event queue, and the collection single-throw event corresponding to the collection single-throw event is added into the event queue;
And if the completion time of the set single-throw event is reached, the set single-throw event is moved out of the event queue.
Optionally, removing the pick task event from the event queue, and adding the aggregate single-throw event corresponding to the pick aggregate single event to the event queue, including:
removing the current picking task event from the event queue, and judging whether the current picking quantity is smaller than the upper limit of the picking box capacity or not;
if yes, adding the next picking task event corresponding to the picking collection list event into the event queue;
if not, the collection single-project event corresponding to the collection single-project event is added into the event queue.
In addition, according to another aspect of an embodiment of the present invention, there is provided a pick productivity scheduling apparatus including:
the distribution module is used for obtaining a plurality of groups of personnel distribution combinations of each storage area according to personnel distribution conditions of each storage area;
the simulation module is used for distributing combinations to each group of personnel every time a scheduling check time node is reached, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency;
And the scheduling module is used for scheduling the picking productivity of each storage area according to the total production number of each storage area corresponding to the distribution combination of each group of personnel under each scheduling checking time node and the order number completed according to time efficiency.
Optionally, the scheduling module is further configured to:
according to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node;
and dispatching the picking productivity of each storage area according to the personnel allocation condition of each storage area under each dispatching inspection time node.
Optionally, the scheduling module is further configured to: for each scheduling inspection time node, the personnel allocation situation of each storage area under the scheduling inspection time node is determined by adopting the following method:
calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency;
and screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node.
Optionally, the scheduling module is further configured to: for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by adopting the following method:
and according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
Optionally, the allocation module is further configured to: :
and determining personnel allocation conditions of each storage area in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of each storage area.
Optionally, the simulation module is further configured to:
distributing a pick-up collection list event to each picker, and adding the pick-up collection list event into an event queue;
if the completion time of the pick-up collection list event is reached, the pick-up collection list event is moved out of the event queue, and the pick-up task event corresponding to the pick-up collection list event is added into the event queue;
if the completion time of the picking task event is reached, the picking task event is moved out of the event queue, and the collection single-throw event corresponding to the collection single-throw event is added into the event queue;
And if the completion time of the set single-throw event is reached, the set single-throw event is moved out of the event queue.
Optionally, the simulation module is further configured to:
removing the current picking task event from the event queue, and judging whether the current picking quantity is smaller than the upper limit of the picking box capacity or not;
if yes, adding the next picking task event corresponding to the picking collection list event into the event queue;
if not, the collection single-project event corresponding to the collection single-project event is added into the event queue.
According to another aspect of an embodiment of the present invention, there is also provided an electronic device including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods of any of the embodiments described above.
According to another aspect of an embodiment of the present invention, there is also provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method according to any of the embodiments described above.
One embodiment of the above invention has the following advantages or benefits: because the technical means of scheduling the picking productivity of each storage area is adopted, each group of personnel is allocated and combined, continuous event simulation is carried out on the job tasks of the delivery of each storage area, the total production number of each storage area and the order number which is completed according to time efficiency are obtained, and the technical problem of inaccurate productivity scheduling in the prior art is solved. According to the embodiment of the invention, the production conditions of all the storage areas in a subsequent period of time are simulated by a simulation method, so that dynamic short-term prediction is realized, and then the productivity is optimized according to the simulation result, so that the picking productivity in the bin is accurately scheduled.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a pick productivity scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of an initial personnel allocation of a storage area according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of the production of a reservoir after 5 minutes according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main flow of a pick productivity scheduling method according to one referenceable embodiment of the invention;
FIG. 5 is a schematic diagram of the primary modules of a pick productivity scheduler according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 7 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The method for dispatching the picking productivity provided by the embodiment of the invention carries out continuous event simulation on the warehouse job task based on the method of system dynamics, and aims to display the production condition in the warehouse and accurately predict the future running state by a simulation means so as to realize the productivity dispatching.
Fig. 1 is a schematic diagram of the main flow of a pick productivity scheduling method according to an embodiment of the present invention. As an embodiment of the present invention, as shown in fig. 1, the pick productivity scheduling method may include:
and step 101, obtaining a plurality of groups of personnel allocation combinations of each storage area according to personnel allocation conditions of each storage area.
In the embodiment of the invention, the scheduling behavior can be simply abstracted into linear integer programming, and if m storage areas exist in the warehouse, and the total number of personnel in the m storage areas is n, the n personnel are distributed to the m storage areas, so that a plurality of groups of personnel distribution combinations are obtained.
Optionally, step 101 may include: and determining personnel allocation conditions of each storage area in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of each storage area. For example, there are two storage areas s1, s2 in the warehouse, the personnel allocation situation of the two storage areas is x1, x2, and n=x1+x2, and scheduling is performed by adopting an enumeration mode, so as to obtain the binary group (x 1, x 2) - > (0, 10), (1, 9), (10, 0).
And 102, each time a dispatch checking time node is reached, distributing combinations for each group of personnel, and performing continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number finished according to time efficiency.
Let t0 be the scheduled inspection time interval (such as 5 minutes, 10 minutes, half hour, etc.) set in the simulation model, and t be the current simulation time, then when the scheduled inspection time node t+t0 is reached, a combination is allocated to each group of personnel, continuous event simulation is performed on the job tasks of each storage area in warehouse through the simulation model, and the simulation model outputs the total production number q1 of each storage area and the order number q2 completed in time.
Optionally, performing continuous event simulation on the job task of the warehouse-out of each storage area, including: distributing a pick-up collection list event to each picker, and adding the pick-up collection list event into an event queue; if the completion time of the pick-up collection list event is reached, the pick-up collection list event is moved out of the event queue, and the pick-up task event corresponding to the pick-up collection list event is added into the event queue; if the completion time of the picking task event is reached, the picking task event is moved out of the event queue, and the collection single-throw event corresponding to the collection single-throw event is added into the event queue; and if the completion time of the set single-throw event is reached, the set single-throw event is moved out of the event queue. For the job task of going out of warehouse, the simulation event can include a pick-up collection event, a pick-up task event and a collection single-throw event, and continuous event simulation is performed on the job task of going out of warehouse of each storage area through a simulation model, so that the total production number of each storage area and the order number completed in time efficiency can be obtained.
Optionally, removing the pick task event from the event queue, and adding the aggregate single-throw event corresponding to the pick aggregate single event to the event queue, including: removing the current picking task event from the event queue, and judging whether the current picking quantity is smaller than the upper limit of the picking box capacity or not; if yes, adding the next picking task event corresponding to the picking collection list event into the event queue; if not, the collection single-project event corresponding to the collection single-project event is added into the event queue.
For example, the simulation model may be designed using the following method:
firstly, order positioning information, warehouse layout information and in-warehouse personnel production efficiency indexes are accessed. The personnel number is set as n, the upper limit of the capacity of the picking box is set as vmax, and the production efficiency (average value or probability distribution) of personnel in the bin is set as ef.
In the simulation model, events can be broken down into three main categories, namely, pick-up order (EA), pick-up order (EB), and pick-up order (EC). When the simulation model is initialized (i.e., pick-and-start), each pick person is assigned an EA and the event is added to an event queue (EventQuue).
In this simulation model, pickering is the main agent, and the initialization pseudocode is as follows:
PickerAgent pa＝new PickerAgent(vmax,ef)；
In the calculation of the simulation model, taking the time step as lambda seconds, it is detected every lambda seconds whether the time element of the queue head event in EventQueue is greater than the current simulation time (simTime). If yes, the event driver (Eventdriver) is not started, and if not, the event driver (Eventdriver) is started.
Assuming that an EA event is assigned to Pickering pa with bin capacity v, the EA initialization pseudocode is as follows:
Event EA＝
EventA.builder().finishTime(t).route(r)
.bindAgent(new PickerAgent(v)).build()；
EventQueue.add(EA)；
where t is the time required to complete the EA event, r is the pick path, and pa is the bound agent.
The time required to complete the EA includes click time, network transmission time, RF gun (a type of handheld device used to scan) response time, pickers validation time, etc., and the pickpaths are calculated by a tsp (Traveling Salesman Problem, tourist questions) algorithm in combination with the actual positioning conditions (e.g., information of storage areas, roadways, etc.).
When simtime=t, the EA event dequeues and EventDriver drives the next event of pa, i.e., the EB event is added to the event queue.
When simTime = t + tb, the EB event dequeues and the EventDriver drives the next event of pa, i.e. the EB event is added to the event queue. Where tb is the time required to complete the EB event. Optionally, ef is some value or average, median, etc. in the efficiency profile. In the simulation model, pickers (i.e., pickers) would follow the pickers path and distribute movement according to the pickers' efficiency, completing the pickers task.
Before adding the EB event to the event queue, the following determination needs to be made:
when pa.v < vmax, eventDriver continues to allocate EB events for pa and add EB events to the event queue.
When pa.v > =vmax, eventDriver assigns EC events to pa and adds the EC events to the event queue.
In actual production, the production efficiency of each link of personnel in a bin is subjected to certain distribution (such as normal distribution), and the inflow of orders is also uncertain, so that in short-term dynamic supply chain planning, one or more prediction models are difficult to accurately solve, the robustness is difficult to ensure, the simulation is operated by adopting a real production operation mode and production parameters, the data can be interpreted, and the flexible scheduling capability is high.
And step 103, dispatching the picking productivity of each storage area according to the total production number of each storage area corresponding to each group of personnel distribution combination under each dispatching inspection time node and the order number completed according to time efficiency.
In the step, the sorting productivity of each storage area can be scheduled according to the total production number q1 of each storage area corresponding to each group of personnel distribution combination under each scheduling checking time node output by the simulation model and the order number q2 completed in time effect, so that the scheduling accuracy is improved.
Optionally, step 103 may include: according to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node; and dispatching the picking productivity of each storage area according to the personnel allocation condition of each storage area under each dispatching inspection time node. And for each dispatching checking time node, screening out the optimal personnel allocation situation of each storage area according to the total production number of each storage area corresponding to each allocation combination output by the simulation model and the order number completed according to time efficiency, so that the picking productivity of each storage area can be dispatched according to the optimal personnel allocation situation of each storage area.
Optionally, for each scheduled inspection time node, determining personnel allocation of the respective storage areas under the scheduled inspection time node by adopting the following method: calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency; and screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node.
One simulation calculation generates a result R from the parameters x1, x 2. Specifically, under a certain scheduling time node output by the simulation model, the total production number of each storage area corresponding to each group of personnel allocation combination and the order number completed according to time efficiency are calculated, the productivity value (namely the result R) corresponding to each personnel allocation combination is calculated, and then the personnel allocation combination with the maximum productivity value is selected from the productivity values, so that productivity scheduling can be performed according to the personnel allocation combination with the maximum productivity value under each scheduling inspection time node.
Optionally, for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by the following method: and according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
The lowest finishing index of the one-time simulation experiment business can be preset as y min The total production number q1 and the order number q2 completed in time effect are weighted as w1 and w2, respectively, the productivity value y of the storage area R1 =w1q1+w2q2+b, and there is a constraint y R1 >＝y min 。
Wherein b is a buffer value allowed by the service.
Alternatively, b typically takes the value ± 10%. Y R1 For enhancing the robustness of the method.
If y R1 <y min This indicates that the personnel have not allocated a combination or that ef (production efficiency) is sampled incorrectly.
If the number of reservoirs is two, then the reservoirs s1, s2 have results y1, y2, respectively, and the result set R can be considered to assign the respective personnel a combination corresponding productivity value y1+y2.
According to the various embodiments described above, it can be seen that the present invention solves the technical problem of inaccurate productivity scheduling in the prior art by performing continuous event simulation on the job tasks of each storage area for delivery to the warehouse by allocating a combination to each group of personnel each time a schedule inspection time node is reached, so as to obtain the total number of production items of each storage area and the number of orders completed in time efficiency. According to the embodiment of the invention, the production conditions of all the storage areas in a subsequent period of time are simulated by a simulation method, so that dynamic short-term prediction is realized, and then the productivity is optimized according to the simulation result, so that the picking productivity in the bin is accurately scheduled.
In the following, two storage areas s1 and s2 are taken as an example, the initial number of people is x1=5, x2=5, the simulation ending time is t2, and the simulation starting time is t1, which is described in detail, and the business scenario can be abstracted as shown in fig. 2.
The method is characterized in that the articles in the bin are uniformly distributed (the simulation mode is a real production operation mode and production parameter operation mode, no matter what the distribution mode is, the result R is only that the numerical value of a service index can be changed, the confidence degree of the result is the confidence degree of a sample, the numbers g1 and g2 of articles to be picked in the storage areas s1 and s2 are respectively 1000 and 500, order timeliness (namely order delivery time) of the two storage areas is not necessarily the same in pairs, the simulation model carries out simulation calculation in every t0=5 (min), the production efficiency of each picker is a fixed value, and ef=10 pieces/min. The actual production situation is shown in fig. 3 after 5 minutes.
At this time, a task t0 is added to the simulation calculation task pool (i.e. from time t1+t0 to time t 2), the scheduler tries to schedule the binary solution, and after calculation by the simulation model, the result R can be obtained t0 And sent to the evaluator. In this scenario, because the scheduler uses enumeration, the evaluator takes the maximum Max of multiple simulation results ({ R) t0 -x) and return parameters x1, x2.
Let b be 0, weight w1 of total production number, w2 of order number completed on time basis is shown in the following table.
| w1 | w2 | |
| s1 | 2 | 3 |
| s2 | 2 | 3 |
s1, s2 is a set of indexes obtained by simulation calculation of the binary group (x 1, x 2) - > (6, 4): total production number q1 and order number q2 completed in time, as shown in the table below.
| q1 | q2 | |
| s1(x1＝6) | 250 | 300 |
| s2(x2＝4) | 250 | 100 |
As can be seen, y=y1+y2=2×250+3×300+2×250+3×100=2200.
If y=2200 is Max ({ R) t0 }), the system returns a solution, x1=6, x2=4, i.e., bin s2 dispatches 1 person to bin s 1.
Fig. 4 is a schematic diagram of the main flow of the pick productivity scheduling method according to one referenceable embodiment of the present invention. As yet another embodiment of the present invention, as shown in fig. 4, the pick productivity scheduling method may include:
and step 401, determining personnel allocation conditions of all the storage areas in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of all the storage areas.
In the embodiment of the invention, the scheduling behavior can be simply abstracted into linear integer programming, and the n personnel are distributed to m storage areas by assuming that m storage areas exist in the warehouse and the total number of personnel in the m storage areas is n, so that a plurality of groups of personnel distribution combinations can be obtained by adopting an enumeration mode.
And step 402, each time a dispatch check time node is reached, a combination is allocated for each group of personnel, and continuous event simulation is carried out on the job tasks of the warehouse-out of each storage area, so as to obtain the total production number of each storage area and the order number completed according to time efficiency.
Step 403, for each scheduling and checking time node, calculating a productivity value corresponding to each group of personnel allocation combination according to the total number of the production areas and the number of orders completed according to time efficiency, and screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the scheduling and checking time node.
Optionally, for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by the following method: and according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
And step 404, scheduling the picking productivity of each storage area according to the personnel allocation condition of each storage area under each scheduling check time node.
In addition, the specific implementation of the method for scheduling the picking capacity according to one embodiment of the present invention is described in detail in the above-mentioned method for scheduling the picking capacity, and thus the description thereof will not be repeated here.
Fig. 5 is a schematic diagram of the main modules of the pick productivity scheduler apparatus according to an embodiment of the present invention, and as shown in fig. 5, the pick productivity scheduler apparatus 500 includes a distribution module 501, a simulation module 502, and a scheduler module 503. The distribution module 501 is configured to obtain a plurality of groups of personnel distribution combinations of each storage area according to personnel distribution conditions of each storage area; the simulation module 502 is configured to perform continuous event simulation on the job tasks of each storage area when the dispatch inspection time node is reached, and allocate a combination to each group of personnel, so as to obtain the total production number of each storage area and the order number completed according to time efficiency; the scheduling module 503 is configured to schedule the picking productivity of each storage area according to the total number of production pieces and the number of orders completed in time effect of each storage area corresponding to each group of personnel allocation combination under each scheduling inspection time node.
Optionally, the scheduling module 503 is further configured to:
according to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node;
and dispatching the picking productivity of each storage area according to the personnel allocation condition of each storage area under each dispatching inspection time node.
Optionally, the scheduling module 503 is further configured to: for each scheduling inspection time node, the personnel allocation situation of each storage area under the scheduling inspection time node is determined by adopting the following method:
calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency;
and screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node.
Optionally, the scheduling module 503 is further configured to: for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by adopting the following method:
And according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
Optionally, the allocation module 501 is further configured to:
and determining personnel allocation conditions of each storage area in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of each storage area.
Optionally, the simulation module 502 is further configured to:
distributing a pick-up collection list event to each picker, and adding the pick-up collection list event into an event queue;
if the completion time of the pick-up collection list event is reached, the pick-up collection list event is moved out of the event queue, and the pick-up task event corresponding to the pick-up collection list event is added into the event queue;
if the completion time of the picking task event is reached, the picking task event is moved out of the event queue, and the collection single-throw event corresponding to the collection single-throw event is added into the event queue;
and if the completion time of the set single-throw event is reached, the set single-throw event is moved out of the event queue.
Optionally, the simulation module 502 is further configured to:
removing the current picking task event from the event queue, and judging whether the current picking quantity is smaller than the upper limit of the picking box capacity or not;
if yes, adding the next picking task event corresponding to the picking collection list event into the event queue;
if not, the collection single-project event corresponding to the collection single-project event is added into the event queue.
According to the various embodiments described above, it can be seen that the present invention solves the technical problem of inaccurate productivity scheduling in the prior art by performing continuous event simulation on the job tasks of each storage area for delivery to the warehouse by allocating a combination to each group of personnel each time a schedule inspection time node is reached, so as to obtain the total number of production items of each storage area and the number of orders completed in time efficiency. According to the embodiment of the invention, the production conditions of all the storage areas in a subsequent period of time are simulated by a simulation method, so that dynamic short-term prediction is realized, and then the productivity is optimized according to the simulation result, so that the picking productivity in the bin is accurately scheduled.
The specific implementation of the picking capacity scheduling device according to the present invention is described in detail in the above-mentioned method for scheduling picking capacity, and thus the description thereof will not be repeated here.
Fig. 6 illustrates an exemplary system architecture 600 in which a pick productivity scheduling method or pick productivity scheduling apparatus of an embodiment of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 is used as a medium to provide communication links between the terminal devices 601, 602, 603 and the server 605. The network 604 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 605 via the network 604 using the terminal devices 601, 602, 603 to receive or send messages, etc. Various communication client applications such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 601, 602, 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (by way of example only) providing support for shopping-type websites browsed by users using terminal devices 601, 602, 603. The background management server may analyze and process the received data such as the article information query request, and feedback the processing result (e.g., the target push information, the article information—only an example) to the terminal device.
It should be noted that, the method for scheduling the picking capacity according to the embodiment of the present invention is generally performed by the server 605, and accordingly, the device for scheduling the picking capacity is generally disposed in the server 605. The method for scheduling picking capacity provided by the embodiment of the present invention may also be performed by the terminal devices 601, 602, 603, and accordingly, the device for scheduling picking capacity may be provided in the terminal devices 601, 602, 603.
It should be understood that the number of terminal devices, networks and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, there is illustrated a schematic diagram of a computer system 700 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 701.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer programs according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes an allocation module, a simulation module, and a scheduling module, where the names of the modules do not constitute a limitation on the module itself in some cases.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: obtaining a plurality of groups of personnel allocation combinations of each storage area according to personnel allocation conditions of each storage area; each time a dispatch checking time node is reached, distributing combinations for each group of personnel, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency; and dispatching the picking productivity of each storage area according to the total production number of each storage area and the order number completed according to time efficiency corresponding to each group of personnel distribution combination under each dispatching inspection time node.
According to the technical scheme of the embodiment of the invention, each time the dispatching inspection time node is reached, each group of personnel is allocated and combined, continuous event simulation is carried out on the job tasks of the warehouse in each storage area, the total production number of each storage area and the order number which is completed according to time efficiency are obtained, and therefore the technical means of dispatching the picking productivity of each storage area are adopted, and the technical problem of inaccurate productivity dispatching in the prior art is solved. According to the embodiment of the invention, the production conditions of all the storage areas in a subsequent period of time are simulated by a simulation method, so that dynamic short-term prediction is realized, and then the productivity is optimized according to the simulation result, so that the picking productivity in the bin is accurately scheduled.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method of scheduling pick productivity, comprising:
obtaining a plurality of groups of personnel allocation combinations of each storage area according to personnel allocation conditions of each storage area;
each time a dispatch checking time node is reached, distributing combinations for each group of personnel, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency;
dispatching the picking productivity of each storage area according to the total production number of each storage area and the order number completed according to time efficiency corresponding to each group of personnel distribution combination under each dispatching inspection time node;
scheduling the picking productivity of each storage area according to the total production number of each storage area and the order number completed according to time efficiency corresponding to each group of personnel distribution combination under each scheduling checking time node, comprising the following steps:
According to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node;
scheduling the picking productivity of each storage area according to the personnel allocation condition of each storage area under each scheduling and checking time node;
for each scheduling inspection time node, the personnel allocation situation of each storage area under the scheduling inspection time node is determined by adopting the following method:
calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency;
screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node;
for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by adopting the following method:
and according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
2. The method of claim 1, wherein deriving a plurality of groups of people allocation combinations for each storage area based on people allocation for each storage area comprises:
and determining personnel allocation conditions of each storage area in an enumeration mode, so as to obtain a plurality of groups of personnel allocation combinations of each storage area.
3. The method of claim 1, wherein performing continuous event simulation of the job tasks for the respective storage areas comprises:
distributing a pick-up collection list event to each picker, and adding the pick-up collection list event into an event queue;
if the completion time of the pick-up collection list event is reached, the pick-up collection list event is moved out of the event queue, and the pick-up task event corresponding to the pick-up collection list event is added into the event queue;
if the completion time of the picking task event is reached, the picking task event is moved out of the event queue, and the collection single-throw event corresponding to the collection single-throw event is added into the event queue;
and if the completion time of the set single-throw event is reached, the set single-throw event is moved out of the event queue.
4. The method of claim 3, wherein moving the pick task event out of the event queue and adding a collective single-throw event corresponding to the pick collective single event to the event queue comprises:
removing the current picking task event from the event queue, and judging whether the current picking quantity is smaller than the upper limit of the picking box capacity or not;
if yes, adding the next picking task event corresponding to the picking collection list event into the event queue;
if not, the collection single-project event corresponding to the collection single-project event is added into the event queue.
5. A pick productivity scheduler, comprising:
the distribution module is used for obtaining a plurality of groups of personnel distribution combinations of each storage area according to personnel distribution conditions of each storage area;
the simulation module is used for distributing combinations to each group of personnel every time a scheduling check time node is reached, and carrying out continuous event simulation on the job tasks of the warehouse-out of each storage area to obtain the total production number of each storage area and the order number completed according to time efficiency;
the scheduling module is used for scheduling the picking productivity of each storage area according to the total production number of each storage area and the order number which is completed according to time efficiency and is corresponding to each group of personnel distribution combination under each scheduling checking time node;
The scheduling module is further configured to:
according to the total production number of each storage area corresponding to each group of personnel allocation combination under each scheduling and checking time node and the order number completed according to time efficiency, respectively determining personnel allocation conditions of each storage area under each scheduling and checking time node;
scheduling the picking productivity of each storage area according to the personnel allocation condition of each storage area under each scheduling and checking time node;
the scheduling module is further configured to: for each scheduling inspection time node, the personnel allocation situation of each storage area under the scheduling inspection time node is determined by adopting the following method:
calculating productivity values corresponding to each group of personnel allocation combinations according to the total production number of each storage area and the order number completed according to time efficiency;
screening out the personnel allocation combination with the maximum productivity value, thereby obtaining the personnel allocation condition of each storage area under the dispatching inspection time node;
the scheduling module is further configured to: for each group of personnel allocation combinations, the productivity values corresponding to the group of personnel allocation combinations are calculated by adopting the following method:
and according to the preset number weight and order weight, carrying out weighted summation on the total production number of each storage area and the order number completed according to time efficiency, and calculating to obtain the productivity value corresponding to the personnel allocation combination.
6. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-4.
7. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010079850.0A CN113222310B (en) | 2020-02-04 | 2020-02-04 | Goods picking productivity scheduling method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010079850.0A CN113222310B (en) | 2020-02-04 | 2020-02-04 | Goods picking productivity scheduling method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113222310A CN113222310A (en) | 2021-08-06 |
| CN113222310B true CN113222310B (en) | 2023-11-07 |
Family
ID=77085602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010079850.0A Active CN113222310B (en) | 2020-02-04 | 2020-02-04 | Goods picking productivity scheduling method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113222310B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN108090708A (en) * | 2018-02-07 | 2018-05-29 | 北京小度信息科技有限公司 | Distribution strategy test method, device, electronic equipment and storage medium |
| CN108345970A (en) * | 2017-01-23 | 2018-07-31 | 北京京东尚科信息技术有限公司 | Logistic storage simulation method, platform and system |
| CN108764579A (en) * | 2018-06-01 | 2018-11-06 | 成都交大光芒科技股份有限公司 | A kind of storage multi-robotic task dispatching method based on congestion control |
| CN110111048A (en) * | 2019-04-29 | 2019-08-09 | 西安电子科技大学 | Order taking responsibility dispatching method in warehousing system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8190463B2 (en) * | 2000-09-06 | 2012-05-29 | Masterlink Corporation | System and method for managing mobile workers |
| US8689220B2 (en) * | 2011-11-30 | 2014-04-01 | International Business Machines Corporation | Job scheduling to balance energy consumption and schedule performance |
| WO2016132534A1 (en) * | 2015-02-20 | 2016-08-25 | 株式会社日立物流 | Warehouse management system, warehouse, and warehouse management method |
-
2020
- 2020-02-04 CN CN202010079850.0A patent/CN113222310B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN108345970A (en) * | 2017-01-23 | 2018-07-31 | 北京京东尚科信息技术有限公司 | Logistic storage simulation method, platform and system |
| CN108090708A (en) * | 2018-02-07 | 2018-05-29 | 北京小度信息科技有限公司 | Distribution strategy test method, device, electronic equipment and storage medium |
| CN108764579A (en) * | 2018-06-01 | 2018-11-06 | 成都交大光芒科技股份有限公司 | A kind of storage multi-robotic task dispatching method based on congestion control |
| CN110111048A (en) * | 2019-04-29 | 2019-08-09 | 西安电子科技大学 | Order taking responsibility dispatching method in warehousing system |
Non-Patent Citations (2)
| Title |
|---|
| Scheduling multiple agents for picking products in a warehouse;J.I.U. Rubrico et al.;《Proceedings 2006 IEEE International Conference on Robotics and Automation》;第1438-1443页 * |
| 基于计算机三维建模技术的物流仓储管理仿真研究;蒋爱先;《物流技术》(第24期);第83-86页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113222310A (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9020829B2 (en) | Quality of service aware scheduling for composite web service workflows | |
| US8140441B2 (en) | Workflow management in a global support organization | |
| CN107845012B (en) | Order processing method and device | |
| CN110371548B (en) | Goods warehousing method and device | |
| US11593735B2 (en) | Automated and efficient personal transportation vehicle sharing | |
| US11321634B2 (en) | Minimizing risk using machine learning techniques | |
| CN109345166B (en) | Method and apparatus for generating information | |
| CN111507651A (en) | Order data processing method and device applied to man-machine mixed warehouse | |
| Joseph et al. | Effects of flexibility and scheduling decisions on the performance of an FMS: simulation modelling and analysis | |
| CN109934427B (en) | Method and device for generating item distribution scheme | |
| CN115202847A (en) | Task scheduling method and device | |
| Girish et al. | Scheduling job shop associated with multiple routings with genetic and ant colony heuristics | |
| CN113379177A (en) | Task scheduling system and method | |
| CN112561371B (en) | Scheduling determination method and device, electronic equipment and storage medium | |
| Zamiri Marvizadeh et al. | Entropy-based dispatching for automatic guided vehicles | |
| US20180285793A1 (en) | Patron presence based workforce capacity notification | |
| CN113222310B (en) | Goods picking productivity scheduling method and device | |
| US20150112742A1 (en) | System and method of automatically allocating tasks | |
| CN113650997A (en) | Method and device for positioning articles in warehouse-out process | |
| CN113222490A (en) | Inventory allocation method and device | |
| Füßler et al. | The cafeteria problem: order sequencing and picker routing in on-the-line picking systems | |
| CN113222304B (en) | Inventory scheduling method and device | |
| Abbes et al. | A new placement optimization approach in hybrid cloud based on genetic algorithm | |
| CN115344359A (en) | Computing power resource allocation method, device, computer readable storage medium and equipment | |
| CN110705951A (en) | Method and device for scheduling warehouse commodities and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |